A three-dimensional dynamic mode decomposition analysis of wind farm flow aerodynamics

TL;DR

This paper applies three-dimensional dynamic mode decomposition to analyze wind farm flow dynamics from large-eddy simulations, revealing how different DMD methods capture flow structures and improve understanding of wind farm aerodynamics.

Contribution

It introduces a combined use of amplitude selection and sparsity-promoting DMD methods for analyzing wind farm flows, showing their effectiveness in reconstructing flow fields and identifying coherent structures.

Findings

SP-DMD captures large coherent structures with fewer modes

AP-DMD requires more modes for accurate flow reconstruction

Horizontal and vertical staggering improves wind farm performance

Abstract

High-fidelity large-eddy simulations are suitable to obtain insight into the complex flow dynamics in extended wind farms. In order to better understand these flow dynamics, we use dynamic mode decomposition (DMD) to analyze and reconstruct the flow field in large-scale numerically simulated wind farms by large-eddy simulations (LES). Different wind farm layouts are considered, and we find that a combination of horizontal and vertical staggering leads to improved wind farm performance compared to traditional horizontal staggering. We analyze the wind farm flows using the amplitude selection (AP) and sparsity-promoting (SP method) DMD approach. We find that the AP method tends to select modes with a small length scale and a high frequency, while the SP method selects large coherent structures with low frequency. The latter are somewhat reminiscent of modes obtained using proper orthogonal decomposition (POD). We find that a relatively limited number of SP-DMD modes is sufficient to accurately reconstruct the flow field in the entire wind farm, whereas the AP-DMD method requires more modes to achieve an accurate reconstruction. Thus, the SP-DMD method has a smaller performance loss compared to the AP-DMD method in terms of the reconstruction of the flow field.